SU‐E‐T‐782: Using Light Output From Doped Plastic Scintillators to Resolve the Linear Energy Transfer Spectrum of Clinical Electron Beams
Bibliographic record
Abstract
Purpose: This research seeks to develop a portable, clinically‐suitable linear energy transfer (LET) detector. In radiotherapy, absorbed dose is commonly used to measure the amount of delivered radiation, though, it is not a good indicator of actual biological damage. LET is the energy absorbed per unit length by a medium along charged particle's pathway; studies have shown that LET correlates well with relative biological effectiveness (RBE). Methods: According to Birks’ law, light output of plastic scintillators is stopping‐power dependent. This dependency can be varied through doping by various high‐Z elements. By measuring light output signals of differently doped plastic scintillators (represented by column vector S, where each row corresponds to different scintillator material), the fluence of charged particles of a given LET (represented by column vector Φ, where each row corresponds to different LET bins) can be unfolded by S=R*Φ where R is system response matrix (each row represents a different scintillator, each column corresponds to different electron LET). Monte Carlo (MC) GEANT4.10.1 was used to evaluate ideal detector response of BC408 scintillating material doped with various concentrations of several high Z dopants. Measurements were performed to validate MC. Results: Signal for 1%‐lead doped BC408 and the non‐doped scintillator was measured experimentally by guiding light emitted by the scintillator (via in‐house made taper, fiber system) to a PMT and then an electrometer. Simulations of 1%Pb‐doped scintillator to non‐doped scintillator revealed 9.3% reduction in light output for 6 MeV electrons which compared well (within uncertainty) with measurements showing 10% reduction (6MeV electrons). Conclusion: Measurements were used to validate MC simulation of light output from doped scintillators. The doping of scintillators is a viable technique to induce LET dependence. Our goal is to use this effect to resolve the LET spectrum of an incident beam.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".